1. Field of the Invention
Exemplary aspects of the present invention relate to a fault prediction method, a fault prediction system, and an image forming apparatus, and more particularly, to a fault prediction method, a fault prediction system, and an image forming apparatus for efficiently predicting a failure of an image forming apparatus.
2. Description of the Related Art
When various conventional devices such as image forming apparatuses malfunction, users cannot use the devices until they are repaired, causing inconvenience to the user. In particular, due to their complexity, electrophotographic image forming apparatuses with their many components tend to suddenly malfunction unless periodic maintenance on each component is performed.
Such malfunctions, or failures, can have several causes. As well as frictional wear from ordinary operation, the presence of harmful materials such as paper powder, wear of a cleaning member such as a cleaning blade and the like, and so on, also can cause the performance of the image forming apparatuses to gradually deteriorate, resulting in reduced imaging quality such as the production of defective images with vertical streaks extending in a direction corresponding to a direction of movement of a surface of an image carrier, blurred images, spotted images, images with background soiling, or the like. However, even these problems do not affect the basic ability of the image forming apparatus to form images, so that the image forming apparatus keeps working until a user encounters such defective image. As a result, the user has to re-input the image formation command as well as fix the problem, thus wasting time and resources.
Therefore, various prediction methods of predicting such failure of an image forming apparatus are provided.
One method predicts failure of an image forming apparatus using an assumed useful life of the apparatus and monitors the operating time of the image forming apparatus. FIG. 1 is a graph illustrating one example of image forming apparatus failure prediction based on time series analysis. A counter counts an accumulated operating time (a counter value) of each component or part of a photoconductor, a development device, or the like. When the counter value reaches a value indicating the end of the useful life of that component or part has been reached as defined based on results of endurance tests or the like, failure of the image forming apparatus is predicted. However, the prediction is not very precise, since the useful life of the image forming apparatus may vary considerably depending on the operating environment and how the apparatus is used.
Another related-art prediction method starts predicting a failure of an image forming apparatus immediately after the image forming apparatus is delivered to a user. The method involves acquiring a reference data group of a plurality of sets of data on operating states of each of a plurality of image forming apparatuses of the same model as the image forming apparatus during test operation thereof. The reference data group is then used as an initial reference data group for determining a formula for calculating an index value used to discriminate among different operating states of the apparatus. After the image forming apparatus starts to work, data of the reference data group is acquired and added thereto.
Yet another known related-art fault prediction method is a boosting method that creates a high-precision device state discriminator by combining a plurality of sub-discriminators having a low degree of precision. In state discrimination of an image forming apparatus using the boosting method, each sub-discriminator determines whether internal information, such as sensor readings, digitized information on operational control of each device, or the like, indicates a normal state or a malfunction state. In this case, a malfunction state or a state of malfunction means either a state of failure (failure state) or a state such that imminent failure of the apparatus is predictable. The readings of each sub-discriminator are weighted and the weighted results are added together to determine whether the image forming apparatus is in a state of malfunction.
The above related-art prediction method can predict a specific failure of a device that is detectable when the device is manufactured. However, the method cannot predict other kinds of fault found to be detectable after manufacturing, that is, during actual usage. Therefore, downtime of the image forming apparatus is not reduced.
Accordingly, there is a need for a technology capable of providing a method of predicting various probable failures of an image forming apparatus to reduce total downtime thereof.